Bone age is an important measure for assessing the skeletal and biological maturity of children. Delayed or increased bone age is a serious concern for pediatricians, and needs to be accurately assessed in a bid to determine whether bone maturity is occurring at a rate consistent with chronological age. In this paper, we introduce a unified deep learning framework for bone age assessment using instance segmentation and ridge regression. The proposed approach consists of two integrated stages. In the first stage, we employ an image annotation and segmentation model to annotate and segment the hand from the radiographic image, followed by background removal. In the second stage, we design a regression neural network architecture composed of a pre-trained convolutional neural network for learning salient features from the segmented pediatric hand radiographs and a ridge regression output layer for predicting the bone age. Experimental evaluation on a dataset of hand radiographs demonstrates the competitive performance of our approach in comparison with existing deep learning based methods for bone age assessment.

骨龄是评估儿童骨骼和生物学成熟度的重要指标。延迟或增加骨龄是儿科医生的严重问题，需要准确评估骨龄，以确定是否以与年龄相一致的速率发生骨成熟。在本文中，我们引入了一个统一的深度学习框架，用于使用实例分割和岭回归进行骨龄评估。提议的方法包括两个集成阶段。在第一阶段，我们使用图像注释和分割模型从放射线图像中注释和分割手，然后进行背景去除。在第二阶段 我们设计了一种回归神经网络体系结构，该体系结构由预训练的卷积神经网络组成，用于从分段的儿科手部X线照片中学习显着特征，以及岭回归输出层，用于预测骨骼年龄。对手部X射线照片数据集的实验评估表明，与现有的基于深度学习的骨龄评估方法相比，我们的方法具有竞争优势。